IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v17y2015i4d10.1007_s10796-014-9526-1.html
   My bibliography  Save this article

On the inaccuracy of numerical ratings: dealing with biased opinions in social networks

Author

Listed:
  • Roberto Centeno

    (Dpto. de Lenguajes y Sistemas Informáticos, UNED)

  • Ramón Hermoso

    (University of Essex)

  • Maria Fasli

    (University of Essex)

Abstract

In this work, we study the potential problems emanating from using numerical ratings in social networks to rank entities regarding their reputation. In particular, we empirically demonstrate how reputation rankings as collected and managed by current systems are likely to be skewed due to subjectivity problems associated with the use of numerical ratings to encapsulate preferences. With the aim of overcoming these problems, we put forward an approach in which users are asked for their opinions about entities in a comparative fashion. In order to select the most appropriate users to be queried, we take advantage of the social structure derived from the interactions among users and entities following a principle of heterogeneity. Finally, we evaluate the proposed approach in the domain of movie ratings by using real datasets collected from different web sites.

Suggested Citation

  • Roberto Centeno & Ramón Hermoso & Maria Fasli, 2015. "On the inaccuracy of numerical ratings: dealing with biased opinions in social networks," Information Systems Frontiers, Springer, vol. 17(4), pages 809-825, August.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:4:d:10.1007_s10796-014-9526-1
    DOI: 10.1007/s10796-014-9526-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-014-9526-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-014-9526-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michael Anderson & Jeremy Magruder, 2012. "Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database," Economic Journal, Royal Economic Society, vol. 122(563), pages 957-989, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.
    2. Matteo Manca & Ludovico Boratto & Salvatore Carta, 2018. "Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system," Information Systems Frontiers, Springer, vol. 20(4), pages 825-839, August.
    3. Matteo Manca & Ludovico Boratto & Salvatore Carta, 0. "Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    4. Gao, Jian & Zhou, Tao, 2017. "Evaluating user reputation in online rating systems via an iterative group-based ranking method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 546-560.
    5. Carlos Iván Chesñevar & Eva Onaindia & Sascha Ossowski & George Vouros, 2015. "Special issue on agreement technologies," Information Systems Frontiers, Springer, vol. 17(4), pages 707-711, August.
    6. Young-Jin Lee & Kellie B. Keeling & Andrew Urbaczewski, 2019. "The Economic Value of Online User Reviews with Ad Spending on Movie Box-Office Sales," Information Systems Frontiers, Springer, vol. 21(4), pages 829-844, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tobias Gesche, 2022. "Reference‐price shifts and customer antagonism: Evidence from reviews for online auctions," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(3), pages 558-578, August.
    2. Grégory Jolivet & Bruno Jullien & Fabien Postel-Vinay, 2014. "Reputation and Pricing on the e-Market: Evidence from a Major French Platform," SciencePo Working papers Main hal-03460312, HAL.
    3. Engström, Per & Forsell, Eskil, 2018. "Demand effects of consumers’ stated and revealed preferences," Journal of Economic Behavior & Organization, Elsevier, vol. 150(C), pages 43-61.
    4. Balat, Jorge & Papageorge, Nicholas W. & Qayyum, Shaiza, 2017. "Positively Aware? Conflicting Expert Reviews and Demand for Medical Treatment," IZA Discussion Papers 10919, Institute of Labor Economics (IZA).
    5. repec:spo:wpmain:info:hdl:2441/1q24hpq2919to8ct061g8p33kn is not listed on IDEAS
    6. Tobias J. Klein & Christian Lambertz & Konrad O. Stahl, 2016. "Market Transparency, Adverse Selection, and Moral Hazard," Journal of Political Economy, University of Chicago Press, vol. 124(6), pages 1677-1713.
    7. Gesche, Tobias, 2018. "Reference Price Shifts and Customer Antagonism: Evidence from Reviews for Online Auctions," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181650, Verein für Socialpolitik / German Economic Association.
    8. Zhuang, Mengzhou & Cui, Geng & Peng, Ling, 2018. "Manufactured opinions: The effect of manipulating online product reviews," Journal of Business Research, Elsevier, vol. 87(C), pages 24-35.
    9. Di Lizia, Adam, 2024. "Social Influence in Online Reviews : Evidence from the Steam Store," The Warwick Economics Research Paper Series (TWERPS) 1505, University of Warwick, Department of Economics.
    10. Dominik Gutt & Jürgen Neumann & Wael Jabr & Dennis Kundisch, 2020. "The Fate of the App: Economic Implications of Updating under Reputation Resetting," Working Papers Dissertations 76, Paderborn University, Faculty of Business Administration and Economics.
    11. Vollaard, Ben & van Ours, Jan C., 2022. "Bias in expert product reviews," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 105-118.
    12. Ayat Mazin Almahmoud, 2019. "The Impact of Social Media Characteristics and Customer Attitude on EWOM: An Empirical Study in Jordanian Banking Sector," Journal of Social Sciences (COES&RJ-JSS), , vol. 8(2), pages 169-188, April.
    13. Zilberman, David & Kaplan, Scott, 2014. "What the Adoption Literature can teach us about Social Media and Network Effects on Food Choices," 2014 AAEA/EAAE/CAES Joint Symposium: Social Networks, Social Media and the Economics of Food, May 29-30, 2014, Montreal, Canada 173076, Agricultural and Applied Economics Association.
    14. Mariana Carrera & Sofia Villas-Boas, 2023. "Generic Aversion and Observational Learning in the Over-the-Counter Drug Market," American Economic Journal: Applied Economics, American Economic Association, vol. 15(3), pages 380-410, July.
    15. Feng, Andy & Graetz, Georg, 2017. "A question of degree: The effects of degree class on labor market outcomes," Economics of Education Review, Elsevier, vol. 61(C), pages 140-161.
    16. Dominik Gutt, 2018. "In the Eye of the Beholder? Empirically Decomposing Different Economic Implications of the Online Rating Variance," Working Papers Dissertations 40, Paderborn University, Faculty of Business Administration and Economics.
    17. repec:spo:wpmain:info:hdl:2441/5fb16v625i8vdbgdiskfbht5i5 is not listed on IDEAS
    18. Bensnes, Simon & Huitfeldt, Ingrid, 2021. "Rumor has it: How do patients respond to patient-generated physician ratings?," Journal of Health Economics, Elsevier, vol. 76(C).
    19. Anna Kerkhof, 2020. "Advertising and Content Differentiation: Evidence from YouTube," CESifo Working Paper Series 8697, CESifo.
    20. Limin Fang, 2022. "The Effects of Online Review Platforms on Restaurant Revenue, Consumer Learning, and Welfare," Management Science, INFORMS, vol. 68(11), pages 8116-8143, November.
    21. Klein, T.J. & Lambertz, C. & Stahl, K., 2013. "Adverse Selection and Moral Hazard in Anonymous Markets," Discussion Paper 2013-032, Tilburg University, Center for Economic Research.
    22. Zemin (Zachary) Zhong, 2022. "Chasing Diamonds and Crowns: Consumer Limited Attention and Seller Response," Management Science, INFORMS, vol. 68(6), pages 4380-4397, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:17:y:2015:i:4:d:10.1007_s10796-014-9526-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.